Investing in nature: Identifying leaders and laggards

To illustrate the value of company-specific nature data, Bloomberg and M&G worked together to apply a practical screening to the 30 largest firms in the Bloomberg World Packaged Food Index, with focus on deforestation and water. The analysis was based on M&G quant modelling and investment expertise, combined with Bloomberg nature-related data and analytics.  

On deforestation we assessed two dimensions: 

• Exposure to deforestation risk: based on revenue ties to FRCs, commodity production and sourcing, supply chain exposure and operating in areas of high biodiversity intactness. 

• Mitigation efforts: using publicly disclosed policies, governance, and FRC-specific certifications (eg RSPO for palm oil) and measures of traceability. 

The chart above identifies the highest exposure and weaker mitigation on the top right, while the bubble size denotes capitalisation. The two anonymised examples in the chart illustrate the range of outcomes: 

• Company A: An Asian meat processor with exposure to two FRCs but minimal disclosed mitigation. A large player in a high-risk, low-action quadrant – raising red flags for engagement or risk pricing. 

• Company B: A European packaged food company with high exposure across six FRCs—but strong mitigation including clear governance, traceability, and policy commitments. Despite exposure, mitigation reduces its risk profile. 

On water we assessed two dimensions: 

• Water inefficiency: using publicly disclosed information, we assessed water consumption efficiency, expressed as the inverse ratio of water consumption over withdrawal. For instance, if a company is using 20% of the water it withdraws, their water inefficiency is 80%.  
• Water stress: The percentage of a companies’ physical assets that are located in areas of high or extremely high water stress, measured by combining Bloomberg physical asset locations with the World Resources Institute’s water stress data.

The second chart below identifies companies operating at high water stress and least efficient in utilising the freshwater they extract on the top right. 

The two anonymised examples illustrate the range of outcomes: 

• Company A: An Asian-based multinational food processing and retailing company whose operations include water-intensive dairy processing. Its plants operate in catchments where limited water resources compete with agricultural activities.  

• Company B: A south-east Asian multinational food and beverage conglomerate with diverse operations including consumer food products. Its operations include plants in the tropics were water supply stress is less material.   

While outlier identification adds value to the investment process, it comes with limitations. Nature-related data remains unevenly reported. Companies that disclose such metrics tend to be those actively measuring and managing their nature-related impacts, which can bias results. As a result, negative outliers may reflect poor performance – or simply a lack of disclosure. Though estimates help bridge data gaps, they are no substitute for a true understanding of company-level nature dependencies and impacts. By factoring in disclosure quality, the model implicitly penalises non-reporting and supports stewardship efforts to encourage greater transparency. 

While a degree of caution is therefore recommended, imperfect datasets shouldn’t be a barrier to using what’s currently available, so action does not have to wait. By focusing on company-specific data aligned with financially material themes, investors can begin identifying potential nature-related leaders and laggards and leverage these insights to inform engagement priorities 

With the right framing, investors can simplify complexity, build relevance into analysis, and take the first steps towards real integration of nature risk. 

Our recommendations for investors would be to: 

• Choose a starting point: Pick a theme that’s material to your portfolio and assess in depth—don’t try to solve everything. 

• Work with what’s available: In the absence of perfect data, useful insights can still be found. 

• Push for better disclosure: Engagement works best when rooted in tangible, company-specific insights. 

To find out more about how to perform this type of analysis with Bloomberg data, reach out  to our Market Specialist ble finance solutions, and on its nature and biodiversity data, which covers up to 45,000 companies, is available here.